1. 自定义inputFormat

1.1 需求

无论hdfs还是mapreduce，对于小文件都有损效率，实践中，又难免面临处理大量小文件的场景，此时，就需要有相应解决方案

 

1.2 分析

小文件的优化无非以下几种方式：

1、 在数据采集的时候，就将小文件或小批数据合成大文件再上传HDFS

2、 在业务处理之前，在HDFS上使用mapreduce程序对小文件进行合并

3、 在mapreduce处理时，可采用combineInputFormat提高效率

 

实现

 

本节实现的是上述第二种方式

 

程序的核心机制：

 

自定义一个InputFormat

 

改写RecordReader，实现一次读取一个完整文件封装为KV

 

在输出时使用SequenceFileOutPutFormat输出合并文件

 

 

 

代码如下：

 

自定义InputFromat

 

 

package cn.itcast.bigdata.combinefile;import java.io.IOException;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.BytesWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.mapreduce.InputSplit;import org.apache.hadoop.mapreduce.JobContext;import org.apache.hadoop.mapreduce.RecordReader;import org.apache.hadoop.mapreduce.TaskAttemptContext;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;public class WholeFileInputFormat extends FileInputFormat
     
      {    @Override    protected boolean isSplitable(JobContext context, Path file) {        return false;    }    @Override    public RecordReader
      
        createRecordReader(            InputSplit split, TaskAttemptContext context) throws IOException,            InterruptedException {        WholeFileRecordReader reader = new WholeFileRecordReader();        reader.initialize(split, context);        return reader;    }}
      
     

 

 

package cn.itcast.bigdata.combinefile;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FSDataInputStream;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.BytesWritable;import org.apache.hadoop.io.IOUtils;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.mapreduce.InputSplit;import org.apache.hadoop.mapreduce.RecordReader;import org.apache.hadoop.mapreduce.TaskAttemptContext;import org.apache.hadoop.mapreduce.lib.input.FileSplit;/** *  * RecordReader的核心工作逻辑： * 通过nextKeyValue()方法去读取数据构造将返回的key   value * 通过getCurrentKey 和 getCurrentValue来返回上面构造好的key和value *  *  * @author * */class WholeFileRecordReader extends RecordReader
     
       {    private FileSplit fileSplit;    private Configuration conf;    private BytesWritable value = new BytesWritable();    private boolean processed = false;    @Override    public void initialize(InputSplit split, TaskAttemptContext context)            throws IOException, InterruptedException {        this.fileSplit = (FileSplit) split;        this.conf = context.getConfiguration();    }    @Override    public boolean nextKeyValue() throws IOException, InterruptedException {        if (!processed) {            byte[] contents = new byte[(int) fileSplit.getLength()];            Path file = fileSplit.getPath();            FileSystem fs = file.getFileSystem(conf);            FSDataInputStream in = null;            try {                in = fs.open(file);                IOUtils.readFully(in, contents, 0, contents.length);                value.set(contents, 0, contents.length);            } finally {                IOUtils.closeStream(in);            }            processed = true;            return true;        }        return false;    }                @Override    public NullWritable getCurrentKey() throws IOException,            InterruptedException {        return NullWritable.get();    }    @Override    public BytesWritable getCurrentValue() throws IOException,            InterruptedException {        return value;    }    /**     * 返回当前进度     */    @Override    public float getProgress() throws IOException {        return processed ? 1.0f : 0.0f;    }    @Override    public void close() throws IOException {        // do nothing    }}
     

package cn.itcast.bigdata.combinefile;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.BytesWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.InputSplit;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.FileSplit;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;import org.apache.hadoop.util.GenericOptionsParser;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;public class SmallFilesToSequenceFileConverter extends Configured implements Tool {    static class SequenceFileMapper extends            Mapper
     
       {        private Text filenameKey;        @Override        protected void setup(Context context) throws IOException,                InterruptedException {            InputSplit split = context.getInputSplit();            Path path = ((FileSplit) split).getPath();            filenameKey = new Text(path.toString());        }        @Override        protected void map(NullWritable key, BytesWritable value,                Context context) throws IOException, InterruptedException {            context.write(filenameKey, value);        }    }    @Override    public int run(String[] args) throws Exception {        Configuration conf = new Configuration();        /*System.setProperty("HADOOP_USER_NAME", "hadoop");*/        String[] otherArgs = new GenericOptionsParser(conf, args)                .getRemainingArgs();        if (otherArgs.length != 2) {            System.err.println("Usage: combinefiles 
       
       
        ");            System.exit(2);        }                Job job = Job.getInstance(conf,"combine small files to sequencefile");        job.setJarByClass(SmallFilesToSequenceFileConverter.class);                job.setInputFormatClass(WholeFileInputFormat.class);        job.setOutputFormatClass(SequenceFileOutputFormat.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(BytesWritable.class);        job.setMapperClass(SequenceFileMapper.class);                FileInputFormat.setInputPaths(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));                return job.waitForCompletion(true) ? 0 : 1;    }    public static void main(String[] args) throws Exception {        args=new String[]{"c:/wordcount/smallinput","c:/wordcount/smallout"};        int exitCode = ToolRunner.run(new SmallFilesToSequenceFileConverter(),                args);        System.exit(exitCode);            }}
       
      
     

 

 自定义outputFormat

需求

现有一些原始日志需要做增强解析处理，流程：

1、 从原始日志文件中读取数据

2、 根据日志中的一个URL字段到外部知识库中获取信息增强到原始日志

3、 如果成功增强，则输出到增强结果目录；如果增强失败，则抽取原始数据中URL字段输出到待爬清单目录

 

 

2.2 分析

程序的关键点是要在一个mapreduce程序中根据数据的不同输出两类结果到不同目录，这类灵活的输出需求可以通过自定义outputformat来实现

 

2.3 实现

实现要点：

1、 在mapreduce中访问外部资源

2、 自定义outputformat，改写其中的recordwriter，改写具体输出数据的方法write()

 

代码实现如下：

数据库获取数据的工具

package cn.itcast.bigdata.mr.logenhance;import java.sql.Connection;import java.sql.DriverManager;import java.sql.ResultSet;import java.sql.Statement;import java.util.HashMap;import java.util.Map;public class DBLoader {    public static void dbLoader(Map
     
       ruleMap) throws Exception {        Connection conn = null;        Statement st = null;        ResultSet res = null;                try {            Class.forName("com.mysql.jdbc.Driver");            conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/urldb", "root", "root");            st = conn.createStatement();            res = st.executeQuery("select url,content from url_rule");            while (res.next()) {                ruleMap.put(res.getString(1), res.getString(2));            }        } finally {            try{                if(res!=null){                    res.close();                }                if(st!=null){                    st.close();                }                if(conn!=null){                    conn.close();                }            }catch(Exception e){                e.printStackTrace();            }        }    }}
     

 

package cn.itcast.bigdata.mr.logenhance;import java.io.IOException;import org.apache.hadoop.fs.FSDataOutputStream;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.RecordWriter;import org.apache.hadoop.mapreduce.TaskAttemptContext;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;/** * maptask或者reducetask在最终输出时，先调用OutputFormat的getRecordWriter方法拿到一个RecordWriter * 然后再调用RecordWriter的write(k,v)方法将数据写出 *  * @author *  */public class LogEnhanceOutputFormat extends FileOutputFormat
     
       {    @Override    public RecordWriter
      
        getRecordWriter(TaskAttemptContext context) throws IOException, InterruptedException {        FileSystem fs = FileSystem.get(context.getConfiguration());        Path enhancePath = new Path("D:/temp/en/log.dat");        Path tocrawlPath = new Path("D:/temp/crw/url.dat");        FSDataOutputStream enhancedOs = fs.create(enhancePath);        FSDataOutputStream tocrawlOs = fs.create(tocrawlPath);        return new EnhanceRecordWriter(enhancedOs, tocrawlOs);    }    /**     * 构造一个自己的recordwriter     *      * @author     *      */    static class EnhanceRecordWriter extends RecordWriter
       
         {        FSDataOutputStream enhancedOs = null;        FSDataOutputStream tocrawlOs = null;        public EnhanceRecordWriter(FSDataOutputStream enhancedOs, FSDataOutputStream tocrawlOs) {            super();            this.enhancedOs = enhancedOs;            this.tocrawlOs = tocrawlOs;        }        @Override        public void write(Text key, NullWritable value) throws IOException, InterruptedException {            String result = key.toString();            // 如果要写出的数据是待爬的url，则写入待爬清单文件 /logenhance/tocrawl/url.dat            if (result.contains("tocrawl")) {                tocrawlOs.write(result.getBytes());            } else {                // 如果要写出的数据是增强日志，则写入增强日志文件 /logenhance/enhancedlog/log.dat                enhancedOs.write(result.getBytes());            }        }        @Override        public void close(TaskAttemptContext context) throws IOException, InterruptedException {            if (tocrawlOs != null) {                tocrawlOs.close();            }            if (enhancedOs != null) {                enhancedOs.close();            }        }    }}
       
      
     

 

package cn.itcast.bigdata.mr.logenhance;import java.io.IOException;import java.util.HashMap;import java.util.Map;import org.apache.commons.lang.StringUtils;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Counter;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class LogEnhance {    static class LogEnhanceMapper extends Mapper
     
       {        Map
      
        ruleMap = new HashMap
       
        ();        Text k = new Text();        NullWritable v = NullWritable.get();        // 从数据库中加载规则信息倒ruleMap中        @Override        protected void setup(Context context) throws IOException, InterruptedException {            try {                DBLoader.dbLoader(ruleMap);            } catch (Exception e) {                e.printStackTrace();            }        }        @Override        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {            // 获取一个计数器用来记录不合法的日志行数, 组名, 计数器名称            Counter counter = context.getCounter("malformed", "malformedline");            String line = value.toString();            String[] fields = StringUtils.split(line, "\t");            try {                String url = fields[26];                String content_tag = ruleMap.get(url);                // 判断内容标签是否为空，如果为空，则只输出url到待爬清单；如果有值，则输出到增强日志                if (content_tag == null) {                    k.set(url + "\t" + "tocrawl" + "\n");                    context.write(k, v);                } else {                    k.set(line + "\t" + content_tag + "\n");                    context.write(k, v);                }            } catch (Exception exception) {                counter.increment(1);            }        }    }    public static void main(String[] args) throws Exception {        Configuration conf = new Configuration();        Job job = Job.getInstance(conf);        job.setJarByClass(LogEnhance.class);        job.setMapperClass(LogEnhanceMapper.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(NullWritable.class);        // 要控制不同的内容写往不同的目标路径，可以采用自定义outputformat的方法        job.setOutputFormatClass(LogEnhanceOutputFormat.class);        FileInputFormat.setInputPaths(job, new Path("D:/srcdata/webloginput/"));        // 尽管我们用的是自定义outputformat，但是它是继承制fileoutputformat        // 在fileoutputformat中，必须输出一个_success文件，所以在此还需要设置输出path        FileOutputFormat.setOutputPath(job, new Path("D:/temp/output/"));        // 不需要reducer        job.setNumReduceTasks(0);        job.waitForCompletion(true);        System.exit(0);    }}